Measuring Robustness and Efficiency in a Connectome-Constrained Fly Visual System Model: Linking an Adversarial and a Perceptual Illusion

Summary

Researchers benchmarked FlyNet, a task-optimized recurrent network constrained by the fly visual system's synaptic connectome, against CNNs, RAFT and CTRNN models on a collision-detection task. FlyNet is adversarially vulnerable but more robust to common video corruptions than RAFT relative to compute, using up to five orders of magnitude fewer FLOPs. Adversarial textures and the reverse-phi illusion disrupt the same low-level motion statistic, suggesting a shared circuit vulnerability.

Related items

• LowYour Robot's Safety Functions Already Work. What If the Input Lies?Similar attack · VicOne Blog
• InfoStructured Confidence Perturbation via Fisher Geometry for Adversarial TrainingSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoA Layer-wise Study of How Style Rewriting Breaks the Calibration of Zero-Shot LLM Fake-News DetectorsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoEvaluating the robustness of explainable AI methods to adversarial attacks in intrusion and malware detectionSimilar attack · OpenAlex (peer-reviewed AI security)
• InfoRL-ATTACK: A Generalized Black-Box Adversarial Attack on Language Models via Reinforcement LearningSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)